Separating spectral signatures for detecting nitrogen, phosphorus and water stress in corn.

摘要

The use of remote sensing for crop production applications is becoming increasingly popular. Pollution of surface and ground water, attributed to poor fertilizer and water management, as well as a need for a better understanding of spatial variability have lead to the increased interest in precision agriculture tools such as remote sensing. Past remote sensing research has focused primarily on nitrogen (N) deficiencies and water stress. Other types of stresses and interactions have not been fully evaluated. Two field experiments were initiated to determine specific wavelengths of reflected electromagnatic radiation that are indicative of phosphorus (P), N, and water stresses and their interactions in corn (Zea mays L.). The field experiments were arranged as randomized complete block designs having factorial arrangement of treatments in irrigated continuous corn. The N by P experiment had four N rates (0, 67, 134, and 269 kg N ha-1) and four P rates (0, 22, 45, and 67 kg P ha-1). The N by water stress experiment had five N rates (0, 45, 90, 134, and 269 kg N ha-1) and three irrigation rates (no water, 0.5 of evapotranspiration (ET) and full irrigation based on ET). Spectral radiance measurements (350–2500nm) were taken at various growth stages and used to predict biomass, grain yield, grain N and P, total plant N and P, and chlorophyll meter readings. Plant P concentration was predicted in early growth stages (before V8) using reflectance in the blue and near infrared (NIR) region while N could be predicted throughout the growing season. Total N concentration was predicted using reflectance primarily in the red and green regions, but critical reflectance wavelengths changed in the presence of other stresses and with differences in growth stage. Estimation of final grain yield was best accomplished by using data from the late July sampling. Hyper-spectral data were able to predict chlorophyll meter readings, biomass, grain yield, and nutrient concentrations, supporting the notion that remote sensing is a reliable technique for detecting stresses.